Application of Decision Tree-Based Twin Support Vector Machines to Classify Dephosphorization in BOF Steelmaking

2020 ◽  
Author(s):  
J. Egas ◽  
K. Chattopadhyay ◽  
S. Barui ◽  
S. Mukherjee ◽  
J. Phull
Keyword(s):  
Support Vector Machines ◽  
Decision Tree ◽  
Support Vector ◽  
Twin Support Vector Machines ◽  
Vector Machines

scholarly journals An Application of Decision Tree-Based Twin Support Vector Machines to Classify Dephosphorization in BOF Steelmaking

Metals ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 25
Author(s):  
Jovan Phull ◽  
Juan Egas ◽  
Sandip Barui ◽  
Sankha Mukherjee ◽  
Kinnor Chattopadhyay
Keyword(s):  
Support Vector Machines ◽  
Decision Tree ◽  
Mean Shift ◽  
Gaussian Mixture ◽  
Partition Ratio ◽  
Support Vector ◽  
Classification Rate ◽  
Twin Support Vector Machines ◽  
Slag Chemistry ◽  
Vector Machines

Ensuring the high quality of end product steel by removing phosphorus content in Basic Oxygen Furnace (BOF) is essential and otherwise leads to cold shortness. This article aims at understanding the dephosphorization process through end-point P-content in BOF steelmaking based on data-mining techniques. Dephosphorization is often quantified through the partition ratio ( l p ) which is the ratio of wt% P in slag to wt% P in steel. Instead of predicting the values of l p , the present study focuses on the classification of final steel based on slag chemistry and tapping temperature. This classification signifies different degrees (‘High’, ‘Moderate’, ‘Low’, and ‘Very Low’) to which phosphorus is removed in the BOF. Data of slag chemistry and tapping temperature collected from approximately 16,000 heats from two steel plants (Plant I and II) were assigned to four categories based on unsupervised K-means clustering method. An efficient decision tree-based twin support vector machines (TWSVM) algorithm was implemented for category classification. Decision trees were constructed using the concepts: Gaussian mixture model (GMM), mean shift (MS) and affinity propagation (AP) algorithm. The accuracy of the predicted classification was assessed using the classification rate (CR). Model validation was carried out with a five-fold cross validation technique. The fitted model was compared in terms of CR with a decision tree-based support vector machines (SVM) algorithm applied to the same data. The highest accuracy (≥97%) was observed for the GMM-TWSVM model, implying that by manipulating the slag components appropriately using the structure of the model, a greater degree of P-partition can be achieved in BOF.

Large-scale pinball twin support vector machines

2021 ◽  
Author(s):  
M. Tanveer ◽  
A. Tiwari ◽  
R. Choudhary ◽  
M. A. Ganaie
Keyword(s):  
Support Vector Machines ◽  
Large Scale ◽  
Support Vector ◽  
Twin Support Vector Machines ◽  
Vector Machines

1-Norm least squares twin support vector machines

2011 ◽  
Vol 74 (17) ◽  
pp. 3590-3597 ◽  
Author(s):  
Shangbing Gao ◽  
Qiaolin Ye ◽  
Ning Ye
Keyword(s):  
Support Vector Machines ◽  
Least Squares ◽  
Support Vector ◽  
Twin Support Vector Machines ◽  
Vector Machines
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